Add trainingscript.py

trainingscript.py

@@ -0,0 +1,54 @@
+from datasets import load_dataset
+from transformers import T5Tokenizer, T5ForConditionalGeneration, Trainer, TrainingArguments
+
+# Initialize the tokenizer
+model_name = "t5-small"
+tokenizer = T5Tokenizer.from_pretrained(model_name)
+
+# Load the dataset with the specific configuration
+dataset = load_dataset("wiki_atomic_edits", "english_insertions", trust_remote_code=True)
+
+# Inspect the dataset splits
+print(dataset.keys())  # Print available dataset splits
+
+# Preprocessing Function
+def preprocess_function(examples):
+    inputs = examples["base_sentence"]
+    targets = examples["edited_sentence"]
+    model_inputs = tokenizer(inputs, max_length=128, truncation=True, padding="max_length")
+    labels = tokenizer(targets, max_length=128, truncation=True, padding="max_length")
+    labels["input_ids"] = [
+        [(label if label != tokenizer.pad_token_id else -100) for label in labels_example]
+        for labels_example in labels["input_ids"]
+    ]
+    model_inputs["labels"] = labels["input_ids"]
+    return model_inputs
+
+# Apply the preprocessing function to the dataset
+tokenized_datasets = dataset.map(preprocess_function, batched=True)
+
+# Initialize the model
+model = T5ForConditionalGeneration.from_pretrained(model_name)
+
+# Set up training arguments
+training_args = TrainingArguments(
+    output_dir="./results",
+    evaluation_strategy="epoch",  # Updated from eval_strategy to evaluation_strategy
+    learning_rate=2e-5,
+    per_device_train_batch_size=4,
+    per_device_eval_batch_size=4,
+    num_train_epochs=3,
+    weight_decay=0.01,
+    logging_dir="./logs",
+)
+
+# Initialize Trainer
+trainer = Trainer(
+    model=model,
+    args=training_args,
+    train_dataset=tokenized_datasets["train"],
+    eval_dataset=tokenized_datasets.get("validation")  # Use .get() to avoid KeyError
+)
+
+# Start training
+trainer.train()